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@harupy I have noticed that there is a warning message that the Keras support will be removed in a future version of MLFlow and will be replaced for TF. When I looked at the TF-related source code for saving the model looks like it supports "SavedModel" format only and it would be quite a bit a work to support HDF5. Not sure if it is worth the effort to support both formats. What do you think?
@balvisio Keras support won't be removed. Sorry for the confusion. In MLflow 2.0.0 (that will be released soon), mlflow.tensorflow will support tf.keras model. We'll merge this PR first, then export the changes in this PR to mlflow.tensorflow before releasing 2.0.0 :)
Got it. Yeah, I got confused because of this warning Autologging support for keras >= 2.6.0 has been deprecated and will be removed. Is not applicable then?
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area/trackingTracking service, tracking client APIs, autologgingrn/featureMention under Features in Changelogs.
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Signed-off-by: Bruno Alvisio [email protected]
Related Issues/PRs
Resolve #7024
What changes are proposed in this pull request?
Allow configuring the format in which the model is saved in
keras.autolog()How is this patch tested?
Added test to load model in 'HDF5' format
Does this PR change the documentation?
Release Notes
Is this a user-facing change?
(Details in 1-2 sentences. You can just refer to another PR with a description if this PR is part of a larger change.)
What component(s), interfaces, languages, and integrations does this PR affect?
Components
area/artifacts: Artifact stores and artifact loggingarea/build: Build and test infrastructure for MLflowarea/docs: MLflow documentation pagesarea/examples: Example codearea/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registryarea/models: MLmodel format, model serialization/deserialization, flavorsarea/pipelines: Pipelines, Pipeline APIs, Pipeline configs, Pipeline Templatesarea/projects: MLproject format, project running backendsarea/scoring: MLflow Model server, model deployment tools, Spark UDFsarea/server-infra: MLflow Tracking server backendarea/tracking: Tracking Service, tracking client APIs, autologgingInterface
area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev serverarea/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Modelsarea/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registryarea/windows: Windows supportLanguage
language/r: R APIs and clientslanguage/java: Java APIs and clientslanguage/new: Proposals for new client languagesIntegrations
integrations/azure: Azure and Azure ML integrationsintegrations/sagemaker: SageMaker integrationsintegrations/databricks: Databricks integrationsHow should the PR be classified in the release notes? Choose one:
rn/breaking-change- The PR will be mentioned in the "Breaking Changes" sectionrn/none- No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" sectionrn/feature- A new user-facing feature worth mentioning in the release notesrn/bug-fix- A user-facing bug fix worth mentioning in the release notesrn/documentation- A user-facing documentation change worth mentioning in the release notes